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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/45281

    Title: An Efficient GA-Based Clustering Technique
    Authors: 林慧珍;Lin, Hwei-jen;Yang, Fu-wen;Kao, Yang-ta
    Contributors: 淡江大學資訊工程學系
    Keywords: Unsupervised Clustering;Genetic Algorithms;Reproduction;Crossover;Mutation;Fitness;Cluster Validity
    Date: 2005-06-01
    Issue Date: 2010-03-26 18:55:24 (UTC+8)
    Publisher: 淡江大學
    Abstract: In this paper, we propose a GA-based unsupervised clustering technique that selects cluster centers directly from the data set, allowing it to speed up the fitness evaluation by constructing a look-up table in advance, saving the distances between all pairs of data points, and by using binary representation rather than string representation to encode a variable number of cluster centers. More effective versions of operators for reproduction, crossover, and mutation are introduced. Finally, the Davies-Bouldin index is employed to measure the validity of clusters. The development of our algorithm has demonstrated an ability to properly cluster a variety of data sets. The experimental results show that the proposed algorithm provides a more stable clustering performance in terms of number of clusters and clustering results. This results in considerable less computational time required, when compared to other GA-based clustering algorithms.
    Relation: 淡江理工學刊=Tamkang journal of science and engineering 8(2), pp.113-122
    DOI: 10.6180/jase.2005.8.2.04
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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